Complete Ghaymah Internship Assessment

postmortem/postmortem-report.md

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+# Postmortem Report
+
+## Incident Summary
+
+**Incident:** Application outage due to repeated OOMKilled events.
+
+**Duration:** 45 minutes
+
+**Impact:**
+- The application was unavailable for users.
+- API requests failed during the outage.
+- User experience was significantly affected.
+
+---
+
+## Timeline
+
+| Time | Event |
+|------|-------|
+| 10:00 | Application deployed successfully. |
+| 10:08 | Memory usage started increasing rapidly. |
+| 10:12 | First OOMKilled event occurred. |
+| 10:13 | Kubernetes restarted the container. |
+| 10:15–10:40 | Continuous OOMKilled restart loop. |
+| 10:42 | Engineering team investigated the issue. |
+| 10:45 | Memory limit increased and application stabilized. |
+
+---
+
+## Root Cause
+
+The application exceeded its allocated memory limit.
+
+The container was repeatedly terminated by Kubernetes with an **OOMKilled** event because memory consumption continued growing beyond the configured limit.
+
+Possible contributing factors:
+
+- Memory leak inside the application.
+- Memory limits configured too low.
+- No Horizontal Pod Autoscaler.
+- Lack of memory usage alerts.
+
+---
+
+## Resolution
+
+The engineering team:
+
+- Increased container memory limits.
+- Restarted the deployment.
+- Verified application health.
+- Monitored memory consumption until stable.
+
+---
+
+## Recommendations
+
+### Immediate Actions
+
+- Configure appropriate memory requests and limits.
+- Investigate memory leaks.
+- Enable memory monitoring.
+- Configure alerting.
+
+### Long-Term Improvements
+
+- Implement Horizontal Pod Autoscaler (HPA).
+- Perform load testing before production.
+- Review memory usage after each deployment.
+- Add automatic scaling policies.
+- Conduct regular post-deployment monitoring.
+
+---
+
+# Auto-Scaling Policy
+
+To prevent similar incidents, the platform should implement:
+
+- Minimum replicas: **2**
+- Maximum replicas: **10**
+- Scale out when:
+  - Memory usage > 70%
+  - CPU usage > 70%
+- Scale in when:
+  - Memory usage < 40%
+  - CPU usage < 40%
+- Cooldown period: **5 minutes**
+- Enable automatic replacement of unhealthy pods.
+
+This policy ensures enough capacity during traffic spikes while reducing resource waste during normal operation.
+
+---
+
+# Early Detection Using Ghaymah Monitoring
+
+The issue can be detected early by monitoring:
+
+- Memory usage
+- CPU usage
+- Container restart count
+- OOMKilled events
+- Pod health status
+- Response time
+- Error rate
+
+Recommended alerts:
+
+- Memory usage above 80%
+- More than 3 container restarts within 10 minutes
+- Pod enters CrashLoopBackOff
+- Health endpoint becomes unavailable
+- Response time exceeds acceptable thresholds
+
+These monitoring practices allow engineers to respond before users experience service interruption.
+
+---
+
+# Lessons Learned
+
+- Proper resource limits are essential.
+- Continuous monitoring is critical.
+- Autoscaling improves application availability.
+- Early alerting reduces downtime.
+- Capacity planning should be reviewed before production deployments.